Internet-based map services have been available for many years, offering functionality including displaying graphical maps (whether image-, line-, or vector-based maps), locating locations by address, and providing travel directions (often illustrated on the above graphical maps). Some map services also maintain a database of points of interest (such as restaurants, stores, and tourist destinations), their respective locations, and other information regarding these points of interest. Searches may be run against this database to identify particular points of interest for a user. For example, a user of a map service may submit a text-based search query for “that restaurant, el cerrito, ca”, resulting in a list of restaurants serving Thai food in or about El Cerrito, Calif., which may also be listed in shown on a graphical map display.
Although the above search may be performed using a conventional index-based search engine, map services have also made use of recommender systems, either instead of or in conjunction with a conventional index-based search engine (e.g., by performing ranking of search results). A popular technique for recommender systems is known as collaborative filtering. An underlying assumption in conventional collaborative filtering systems is that users who have agreed in the past will tend to agree in the future. Collaborative filtering techniques rely upon “interest information” (sometimes referred to as “taste information” or “ratings information”) collected from a plurality of users regarding a plurality of items, such as points of interest. In user-based collaborative filtering, users or groups of users similar to a particular user of interest are identified, and the interest information collected by these users or groups of users are used to predict which items are of interest for the particular user of interest. In item-based collaborative filtering, the similarity of items is determined identified based on the interest information, and is used to suggest items for a particular user of interest. Many other forms and variations of collaborative filtering are known to those skilled in the art.
One issue with conventional recommender systems is that they only take into account the interests of similar users or look to similar items, based on items preferred or selected by a particular user of interest. This tends to have an effect where the user's previously indicated interests reinforce the selection of similar items. For example, if the user has previously indicated an interest for Mexican restaurants, conventional recommender systems will recommend more restaurants serving Mexican or similar food. Accordingly, users may fail to be introduced to and learn that they enjoy items outside of their previously indicated interests.